The present invention relates to intrusion alarm video-processing devices, and in particular, relates to an intrusion alarm video-processing device that detects an intruder by processing a video shot with a monocular camera.
The conventional intruder alarm system is not satisfactory with regard to frequent false alarms, a lack of versatility, i.e., requiring delicate and labor intensive setting adjustment corresponding to monitoring stations. When classical tasks in image processing, such as segmentation, skeleton extraction, recognition, and detection, need to be realized, apparently, difficulties in developing a typical intruder alarm system are in large part due to the presence of various noises due to various kinds of sources.
Inexpensive CMOS sensors are used in almost all surveillance video cameras. However, in even the highest-performance sensor among these sensors, a certain hardware noise mixes into imaging data. There is an inverse correlation between the luminance level and the sensor noise level. Due to this noise, the same two images cannot be taken even if a camera and the environment to be imaged are not moving. Actually, the luminance value or the RGB value of a pixel is observed as a probability variable. Accordingly, the value of a pixel observed as the probability variable should be modeled with an appropriate method. It has been experimentally proved that the sensor noise can be appropriately modeled as white noise.
As a related art underlying the present invention, a moving vehicle detection method by Eremin S. N. is known (see RU (Russian) patent No. 2262661). This method comprises the steps of acquiring a frame, calculating an inter-frame difference, binarizing with a threshold, performing morphological operation, calculating a Sobel operator, storing an initial frame, and updating the background based on a special equation, detecting a difference between a frame and a background, calculating a histogram of images, detecting the maximum luminance, verifying by comparison with an existing object, separating a mixed object, locating a vehicle, and generating a rectangle that expresses a coordinate at which the vehicle may be located within a relevant framing means.
Moreover, as a related art in connection with the present invention, an image recognition method using a Hu invariant moment is known (see Ming-Kuei HU, “Visual Pattern Recognition by Moment Invariants”, IRE Transactions on information theory, 1962, pp. 179-187).
Moreover, a method is known, in which Fourier Mellin transform or a Gabor filter is used as a scale invariable value and these are compared with a dictionary to recognize an object (see Park, H. J., Yang H. S, “Invariant object detection based on evidence accumulation and Gabor features”, Pattern recognition letters 22, pp. 869-882, and Kyrki, V., Kamarainen J. K, “Simple Gabor feature space for invariant object recognition”, Pattern recognition letters 25, No. 3, 2004, pp. 311-318).
Moreover, a corner detection method by Harris is known (see C. Harris and M. Stephens, “A combined corner and edge detector”, Proc. Alvey Vision Conf., Univ. Manchester, 1988, pp. 147-151). In this approach, a detected corner is used as a feature quantity. Any object has a unique set of corner points. Recognition processing is performed by comparing with a positional relationship of corners which an object in a standard image has.
Moreover, there are known a method of applying a Gaussian filter to an image in multi-stages and preparing difference image groups thereof (Laplacian pyramid) (see U.S. Pat. No. 6,141,459), and SIFT (Scale-invariant feature transform) that extracts a scale invariable feature quantity, such as a key point, from the maximum value of these image groups (see David G. Lowe, “Distinctive image features from scale-invariant key points, Journal of Computer Vision, 60, 2, 2004, pp. 91-110).